This state of affairs requires at least four distinct rankings, which is impossible with only two constraints.

it is established that, at k = 2, the largest shatterable sample set has at most one candidate.


KL-divergence is always non-negative and is zero if and only if the two distributions are exactly the same.

where large values indicate higher degrees of syntactic fixedness.


Recall that Fixedness lex (v, n) ∈ [−∞, +∞], and To combine them in the overall fixedness measure, we rescale them, so that they fall in the range [0,1].

fixedness overall (v, n) ∈ [0, 1], where values closer to 1 indicate a higher degree of overall fixedness.


(Recall that, for training, CONTEXT uses instances in canonical form as being idiomatic and those in non-canonical form as being literal.)

in many cases, it is a priori more likely that a token is more similar to the K most similar canonical form instances.


The formulas in Table 9 indicate that if both Sens and PPV are high, then tp fn and tp fp.

most idiomatic usages of expressions in DT I high appear in a canonical form, and most usages in a canonical form are idiomatic.


If the vocabulary in two segments is very similar, the two segments are analyzed as belonging to the same discourse unit; otherwise they are analyzed as two distinct units.

one major difference between the two approaches is the unit of analysis.


Each locative expression (e.g., a PP or NP) is generally assumed to have a unique locative relation and place value.

combinations of pure locative relation markers and prepositions which are specified for place values are ruled out, and no embedded locative relations are allowed in the semantic structure of a single locative expression.


PropBank has been used as the training data in recent semantic role labeling competitions as part of the Conferences on Computational Natural Language Learning Màrquez 2004, 2005).

it is likely to become as influential as FrameNet in computational semantics.


It turns out that grounds and news are often used as the prepositional object in PTB when the sense for on is purpose (or reason).

these words would likely be chosen as collocations for this sense.


Recall that the WordNet synset hierarchy can be viewed as a taxonomy of concepts.

in addition to using collocations in the form of other words, we use collocations in the form of semantic concepts.


{ wealth#4, property#2, belongings#1, holding#2, material possession#1, possession#2 } 3.

{ currency#1, medium of exchange#1, monetary system#1, standard#1, criterion#1, measure#2, touchstone#1, reference point#1, point ofreference#1, reference#3, indicator#2, signal#1, signaling#1, sign#3, communication#2, social relation#1, relation#1, abstraction#6 } the word token money is replaced by 41 synset tokens.


For example, the prior probabilities for the roles would be based on the usages for the given preposition.

we perform experiments illustrating the difference when disambiguating prepositions with a single classifier versus the use of separate classifiers.


In contrast, in a context referring to an abstract concept (i.e., abstraction#6), the difference in the probability distributions for the same roles shows that the temporal interpretation becomes more likely.

these class-based lexical associations capture commonsense usages of the preposition at.


However, in an abstract context, the role time becomes more prominent.

similar behavior to that noted for PTB in Section 3.2.1 occurs with FrameNet.


Likewise, characteristic is the preferred role for of, but category is interpretable.

the judges differed considerably on these cases.


The Romance prepositions used in the translations of English nominal phrases and compounds, may vary in ways that correlate with the semantics.

romance language prepositions will give us another source of evidence for disambiguating the semantic relations in English nominal phrases and compounds.


A special relation here is KINSHIP, which is encoded only by N P N constructions and whose argument order is irrelevant.

the labeling of the semantic arguments for each relation as Arg 1 and Arg 2 is just a matter of convention and they were introduced to provide a consistent guide to the annotators to easily test the goodness-of-fit of the relations.


: Dacȃ are loc, evaluarea Preşedinţiei spaniole va fiîncȃ mai positiva decât pânȃ acum.

the corpus instances used in the corpus analysis phase have the following format: NP En ; NP Es ; NP It ; NP Fr ; NP Port ; NP Ro ; target .


For example, fata-fetei (the girlgirl-GEN), cartea-cȃrţii (the book -book-GEN).

the noun phrase the beauty of the girl, for instance, is translated as frumuseţea fetei ('beauty-the girl-GEN'), and the beauty of a girl as frumuseţea unei fete ('beauty-the of/to a girl').


Based on the literature on prepositions (Lyons 1986;Barker 1998;Ionin, Matushansky, and Ruys 2006) and our own observations, the preposition of/de in both root and synthetic nominal phrases may have a functional or a semantic role, acting as a linking device with no apparent semantic content, or with a meaning of its own.

for the interpretation of these constructions a system must rely on the meaning of preposition and the meaning of the two constituent nouns in particular, and on context Table 7 Mapping between the set of 22 semantic classification categories and the set of English and Romance syntactic constructions on the CLUVI corpus.


In order to classify a given set of examples (members of X), one needs some kind of measure of the similarity (or the difference) between any two given members of X.

the system receives as input an English nominal phrase and compound instances along with their translations in the Romance languages, plus a set of extralinguistic features.


However, our observations on the Romanian training instances in Europarl and CLUVI (captured by features F12 and F13 below) indicate that the choice of syntactic constructions can help in the disambiguation of instances that include such active nouns.

whereas genitive-marked N N compounds identify only the subject (thus encoding EXPERIENCER), the N de/pentru N constructions identify only the object (thus encoding THEME).


The processing method consists of a set of iterative procedures of specialization of the examples on the WordNet IS-A hierarchy.

after a set of necessary specialization iterations, the method produces specialized examples which through supervised machine learning are transformed into sets of semantic rules for the semantic interpretation of nominal phrases and compounds.


The specialization procedure consists of first identifying the features f ij to which correspond more than one semantic relation, then replacing these features with their hyponym synsets.

one feature breaks into several new specialized features.


Our intuition is that the more information we use from other languages for the interpretation of an English instance, the better the results.

we wanted to see the impact of each Romance language on the overall performance.


Because the choice of syntactic constructions in context is governed in part by semantic factors, we focused on English and Romanian because they are the only languages from the set considered here with two productive syntactic options: N N and N P N (English) and genitive-marked N N and N P N (Romanian).

we grouped the English-Romanian parallel instances per each semantic relation and each syntactic construction and checked if the relation was symmetric or not, according to the following definition.


However, even here symmetry is not always possible, being influenced by pragmatic factors (Herskovits 1987) (e.g., we can say the vase on the table, but not the table under the vase-this has to do with the difference in size of the objects indicated by the head and modifier nouns.

a larger object cannot be said to be placed under a smaller one).


The parts are similar to their wholes, but they are not separable from them.

this relation overlaps with the LOCATION relation.


Table 11 summarizes the symmetry properties of all five PART-WHOLE subtypes accompanied by examples.

features such as the semantic classes of the two nouns (F1, F2), and the syntactic constructions in English and Romanian-more specifically, the preposition features for English (F5) and Romanian (F12) and the inflection feature for Romanian (F13)can be used to train a classifier for the identification of the argument order in nominal phrases and compounds encoding different subtypes of PART-WHOLE relations.


We investigated the problem based on cross-linguistic evidence from a set of six languages: English, Spanish, Italian, French, Portuguese, and Romanian.

we presented empirical observations on the distribution of nominal phrases and compounds and the distribution of their meanings on two different corpora, based on two state-of-the-art classification tag sets: Lauer's set of eight prepositions (Lauer 1995) and our list of 22 semantic relations.


This abstract structure can be mapped to a more concrete structure, including space in English, and space or time in Finnish.

in Example (25) there is an ordering in space such that Example (26) is false at the beginning of this ordering, and true towards the end of the ordering.


As objects can be moving, this function has to be time-dependent, hence of type e → (i → r).

if t is a particular time point, t : i, and the term paris of type e is the name of Paris, LOC paris(t), which is of type r, expresses the region of Paris at time t. The LP cannot denote such a simple region, however, but has to be a set of regions, what Kracht calls a neighborhood.


The surface representation does not have to represent all the deeper semantic meaning explicitly, but the deeper meaning should be derivable from the surface representation.

for this particular example, it suffices with a surface representation corresponding to Example (36) since we have demonstrated how Example (42) can be derived deterministically.


This analysis is based on the fact that the alternating preposition introduces the mode, and does not merely disambiguate between modes, as the cofinal reading of over is only available when preceded by an alternating preposition, as shown in Examples (13) and (14).

we end up with the following analysis of alternating prepositions: Alternating.


Recall that empty elements must be syntactically selected.

we constrain the selecting head, whether a verb or a modalizer, to select a preposition with an empty element.


Semantically, however, this is interpreted as modification.

even though sentence (2) gets two different syntactic analyses, they are both ascribed the same semantics (see Example (84)).


Put-verbs allow static and cofinal locatives, but not coinitial locatives.

we follow Kracht (2002) in that locatives are generally adjuncts, but may be selected as LPs under certain circumstances.


The distinguishing characteristic of a visually situated dialog system is that the system has the ability to visually perceive the environment in which a dialog is situated.

these systems use both visual and linguistic contextual information to understand user commands and to generate linguistic descriptions of the environment.


One of the important tasks in this process is resolving the references in the input.

the dialog manager may invoke the reference resolution component.


People often use the spatial location of an object, described using spatial prepositions, when making exophoric references.

in order to interpret these references the system must have access to models of the semantics of the prepositions The mapping performed by the spatial reasoning module from qualitative to geometric representations during the interpretation of a locative expression.


However, neither of these approaches consider the effect that the locations of other objects in the scene have on the proximity.

they cannot distinguish between the different context provided by the two images in Figure 7.


However, unlike CFGs, HHMMs have memory limits on nesting, in the form of a maximum depth D beyond which no expansion may take place.

the expressive power of an HHMM is restricted to the set of regular languages, whereas CFGs may recognize the set of context-free languages; and HHMM recognition is worst-case linear on the length of an utterance, whereas CFG recognition is cubic.


Recall that a reified world model can be cast as a subsumption lattice as described in Section 3.1.2.

the result of conjoining a property l with a context set e can therefore be found by downward traversal of an edge in this lattice labeled l and departing from e. 6 in Figure 4, the set of executables that are read-only would be reachable by traversing a READ-ONLY relation from the set of executables, or by traversing an EX-ECUtABLE relation from the set of read-only objects, or by a composed path READ-ONLY•EXECUtABLE or EXECUtABLE•READ-ONLY from e .


For example, this ontology can be manipulated using directives such as: (1) set Crookston campus homeroom two Clark to sports football captain which are incrementally interpreted by transitioning down the subsumption lattice (e.g., from sports to football to captain) or forking to another part of the lattice (e.g., from Clark to sports).

as an ontology like this is navigated in spoken language, there is a sense in which other referents e at the same level of the ontology as the most recently described referent e, or at higher levels of the ontology than the most recently described entity, should be semantically accessible without restating the ontological context (the path from the root concept e ) shared by e and e. in the context of having recently referred to someone in Homeroom 2 at a particular campus in a school activities database, other students in the same homeroom or other activities at the same campus should be accessible without giving an explicit back up directive at each branch in the ontology.


This acoustic model performs competitively with multi-state triphone models based on multivariate Gaussian mixtures, but has the advantage of using only uniphones with single subphone states.

less of the HMM trellis beam is occupied with subphone variations, so that a larger number of semantically distinct hypotheses may be considered at each frame.


First, this is because the acoustic model used in these experiments was trained on a relatively small corpus (6,000 utterances), which introduces the possibility that the acoustic model was under-trained.

the error rates for both baseline and test systems may be greater here than if a larger training corpus had been used, so the performance gain due to the introduction of referential semantics may be overstated.


The hypothesized context of this directory is then a world state or planning state resulting from these actions.

characterized, the referential semantic decoder is performing a kind of statistical plan recognition (Blaylock and Allen 2005).


From the perspective of Hezbollah, failed to defeat is positive.

the contextual polarity of this phrase ultimately depends on the perspective of who is expressing the sentiment.


Only 880 sentences have a mixture of both positive and negative subjective expressions, whereas 3,234 sentences have a mixture of positive and negative clue instances.

a large number of positive and negative instances are either neutral in context, or they are combining to form more complex polarity expressions.


However, determining which clue instances are part of the same expression and identifying expression boundaries are not the focus of this work.

instead of trying to identify and label each expression, in the following experiments, each clue instance is labeled individually as to its contextual polarity.


Co-occurrences of the verb visit with words that are distributionally similar to country, such as state, city, and region, however, do appear in the corpus.

we may infer that visit-country is also a plausible expression, using some mathematical scheme of similarity-based generalization (Essen and Steinbiss 1992;Dagan, Marcus, and Markovitch 1995;Karov and Edelman 1996;Ng and Lee 1996;Ng 1997;Dagan, Lee, and Pereira 1999;Lee 1999;Weeds and Weir 2005).


A feature represents another word (or term) w with which w co-occurs, and possibly specifies also the syntactic relationship between the two words, as in Grefenstette (1994), Lin (1998), and Weeds and Weir (2005).

a word (or term) w is represented by a feature vector, where each entry in the vector corresponds to a feature f .


For example, Curran (2004) reports that multi-word expressions make up between 14-25% of the synonyms in a gold-standard thesaurus.

in our representation the corpus is first transformed to a set S of dependency relationship instances of the form w,f , where each pair corresponds to a single cooccurrence of w and f in the corpus.


Curran and Moens (2002) argue that, generally, informative features are statistically correlated with their corresponding headword.

they suggest that any statistical test used for collocations is a good starting point for improving featureweight functions.


For example, the word company would correspond to the existential statement "there exists an instance of the concept company in some context."

if in some context "there is a company" (in the sense of "commercial organization") then necessarily "there is a firm" in that context (in the corresponding sense).


On the other hand, "there is an organization" does not necessarily imply the existence of company, since organization might stand for some non-profit association, as well.

we conclude that organization does not entail company.


Because the lexical entailment relation is directional, each candidate pair was duplicated to create two directional pairs, yielding a test set of 3,200 pairs.

for each pair of words, w and v, the two ordered pairs (w, v) and (v, w) were created to be judged separately for entailment in the specified direction (whether the first word entails the other).


These results show that for MI weighting many important features appear further down in the ranked vectors, while for the bootstrapped weighting adding too many features adds mostly noise, since most characteristic features are concentrated at the top ranks.

in addition to better feature weighting, the bootstrapping step provides effective feature reduction, which improves vector quality and consequently the similarity results.


On the other hand, there are only two common features (among the top 100 features) for the incorrect pair country-party, both with quite low ranks (compare with Table 7), while the rest of the common features for this pair did not pass the top 100 cutoff.

table 10 demonstrates a much more accurate similarity list for country, where many incorrect (non-entailing) word similarities, like party and company, were demoted.


It should be noted at this point that these observations regarding feature weight behavior are based on subjective intuition of how characteristic features are for a word meaning, which is quite difficult to assess systematically.

we next propose a quantitative measure for analyzing the quality of feature vector weights.


Yet, some of the words that are distributionally similar to private, like soldier or sergeant, might have occurred with save.

a WSD system may infer that the co-occurrence save private is more likely for the rescuing sense of save because private is distributionally similar to soldier, which did cooccur with this sense of save in the annotated training corpus.


Suppose two algorithms perform equally well on easy instances; their performance on the hard ones is as good as agreement-by-coin-flipping would allow.

the difference in the number of "correct" answers on hard instances for algorithms A and B is a random variable S satisfying S = R i=1 X i where X 1 , .


As described in the previous section, the kernel function is the only task-specific component of the learning algorithm.

to develop a WSD system, we only need to define appropriate kernel functions to represent the domain and syntagmatic aspects of sense distinction and, second, exploit the properties of kernel functions to define a composite kernel to combine and extend the individual kernels.


There exist very efficient algorithms to perform the SVD process on sparse matrices, allowing us to perform this operation on large corpora in a very limited time and with reduced memory requirements.

6 we can define the domain kernel to estimate the domain similarity between the contexts of the words to be disambiguated.


Creating good-quality reference texts is an expensive endeavor, especially in domains (such as summaries of clinical data) where texts must be written by skilled domain experts.

we suspect that it may be difficult in many cases to justify creating large corpora of reference texts solely for the purposes of automatic evaluation of NLG systems.


However, our CKY factorization algorithm will give a factorization with n/2 discontinuous spans in one dimension.

in the worst case, the number of spans found by the cubic-time algorithm grows with n, even when a constant number of spans is possible, implying that there is no approximation ratio on how close the algorithm will get to the optimal solution.


A strategy for parsing (or decoding) the entire rule must build up the complete set of the rule's children through a sequence of such combinations.

a parsing strategy corresponds to a recursive partitioning of the rule's children, that is, an unordered rooted tree having the child nonterminals as leaves.


(In the case of decoding, each edge has a multiplicity of (m − 1) on the output language side.)

the left edge in each span of Y corresponds to the left edge of a span in X or to the right edge of a span in C. Y has at most one span for each span in C ∪ X , so y ≤ c + x.


We know that y ≤ a + b since Y was formed from A and B.

so parsing X → Y C also takes no more time than the original rule X → A B C. By induction over the number of subsets, a rule having any number of subsets on the righthand side can be converted into a series of binary rules.


Empirical study shows that the vast majority of syntactic reorderings, at least between languages like English and Chinese, can be efficiently decomposed into hierarchical binary reorderings.

decoding with n-gram models can be fast and accurate, making it possible for our syntax-based system to overtake a comparable phrase-based system in BLEU score.


The second difference is a result of the first, as a good candidate for sentence retrieval contains terms that appear in a request sentence, but is also likely to contain additional terms that expand on the matching terms (Figure 1(b)).

recall is calculated for each response sentence with respect to each request sentence as follows (since TF=1 yielded the best result for the request-response option in Doc-Ret, we also use it for Sent-Ret).


In contrast to prediction probability, SVM reliability reflects its overall performance (on the training data), and is independent of particular requests.

the SVM for SC 1 has a higher reliability than that for SC 3 , because it is easier for an SVM to learn when SC 1 is appropriate (predominantly from the presence of the words faulty and repair).


In contrast to Doc-Pred, Sent-Pred can find regularities at the sub-document level, and generate partial responses, which typically omit inappropriate sentences that may be included by Doc-Pred.

the responses generated by Sent-Pred have a consistently high precision (average 0.94, standard deviation 0.13), but this can be at the expense of recall, which explains the lower F-score (compared to Doc-Pred).


However, in our situation, there is not always one single winner (two methods can perform similarly well for a given request), and there are different ways to pick winners (for example, based on F-score or precision).

such an approach would require the utilization of subjective heuristics for creating labels, which would significantly influence what is being learned.


As we saw from Cluster 11 in Figure 8(b), the estimated performance can be low for all the response-generation methods.

we also test these configurations in Table 6 Precision and F-score for the meta-learning methods averaged over the corpus.


As mentioned in Section 4.2, we consider precision to be an important practical criterion because it does not penalize partial but correct responses.

we "implement" our practical system by selecting only responses whose estimated precision is above 0.8.


In contrast to these two systems, our system employs methods that are not comparable, because they use different metrics.

we need to learn from experience when to use each method.


The surprisal framework models allocation of processing resources (in this case, activation) among disjunctions of competing hypotheses, which are maintained for some amount of time in parallel, whereas the framework described here can be taken to model the allocation of processing resources (in this case, memory elements) among conjunctions of incompletely recognized constituents within each competing hypothesis.

2 in this view, there are two ways to simultaneously activate multiple concepts: disjunctively (sharing activation among competing hypotheses) and conjunctively (sharing activation among unassimilated constituents within a hypothesis).


11 Conjunctions are decomposed into purely right-branching structures using nonterminals appended with a "-LIST" suffix: This right-branching decomposition of conjoined lists is motivated by the general preference in English toward right branching structure, and the distinction of right children as "-LIST" categories is motivated by the asymmetry of conjunctions such as and and or generally occurring only between constituents at the end of a list, not at the beginning.

(in decomposing coffee, tea or milk, the words tea or milk form an NP-LIST constituent, whereas the words coffee, tea do not.)


The resulting calculation yields a frequency-weighted ontological distance between the two sets of concepts.

we view a text not as a set of items to be compared individually to those in another set (with those individual distances then somehow combined, e.g., as in Corley and Mihalcea [2005]), but rather as a distribution of "mass" within a graph that encodes the semantic relations across the two sets, and use a weighted graph-based approach that captures the aggregate distance between the two frequency masses.


We have now seen that a node can belong to both profiles, and its characterization more accurately is stated in terms of net supply/demand.

for example, a square node may belong to just the supply profile or to both the supply and demand profile; the defining factor is that it has a net supply.


This is due to the fact that higher frequency verbs typically occur with a wider range of nouns, leading to a more dispersed semantic profile (i.e., a larger number of concepts).

the best threshold for separating the alternating and non-alternating verbs differs across the frequency bands, and the threshold for all verbs together lies in between the thresholds for the high and low frequency bands.


To begin, we note that any subgraph of the WordNet hypernym hierarchy is hierarchical itself.

any region of the ontology that contains some profile mass is a hierarchy rooted at some common ancestor of those profile nodes.


As noted earlier, our definition of profile density depends on identifying a suitable set of ancestor nodes of the concept nodes in the profile: the aggregate distance of the ancestors to the profile nodes indirectly indicates the degree to which the profile nodes are spatially clustered close together.

given a profile P, we need to find A, the set of nodes that are ancestors of the profile nodes d ∈ P. (The nodes a ∈ A correspond to the hollow triangles indicated in Figure 8 and Figure 9.)


In addition, documents have internal discourse structure and sentences are often inter-dependent.

even if a summary contains sentences found in one or more reference summaries, it does not always mean that it is advisable to include those sentences in the summary in question.


14 No adjustments were made to either system to make them more suitable for summarizing stories.

the comparison is not wholly fair, but-in the absence of systems similar to ours-it was the only possibility to compare our summarizer with the state-of-the-art in the community and to verify whether genre-specific methods are useful in summarizing fiction.


For example, although meld is a blend of melt and weld, the current frequency of the phrase melt and weld may not be as common as the source word co-occurrences for newly coined expressions.

an important step to support further research on blends is to develop a data set of recent neologisms that are judged to be lexical blends.


As an example of the kind of changes that were made, in Example (4), we had predicted that the speaker would use a L-H% boundary (a continuation rise) prior to the word but, however she instead consistently used a low boundary tone in this location.

we changed all the APML files for sentences with this pattern to include a L-L% compound edge tone at that point.


On the other hand, because our method lacks scores, the location of a text must always be searched for.

the previous methods require only O(1) time for searching, whereas our method requires a certain amount of time before a response is obtained.


Such results show how rarely these anomalies occur in our method.

our choice of total ordering seems relevant.


Another problem is that because we do not possess the training data used in previous work, we could only test with what we have listed in Table 2.

we compared the performance using TD2-E-M as training data.


Second, the effect of combination should be considered; that is, the number of pairs whose maximum number is 2 × |L d | × |L e | for a given N. When N = 600, this amounts to 720,000 training pairs, which is too large in terms of the time required for training.

by fixing N = 100, we tested the learning effect for numbers of training pairs up to 2 × 100 × {1, 5, 10, 50, 100}.


The teaching levels do not necessarily correspond to standard school grades for natives.

they prefer that a system outputs texts directly as in Terrace, because this means they Average Error of Search do not have to interpret some score which is difficult to assess.


In any case, for all texts the response time was less than a second.

although our method does have a complexity disadvantage, the results of this evaluation suggest this is not a serious problem.


The structural features that we propose are based on the idea that some parts of the question and the answer passage are more important for relevance ranking than other parts.

our re-ranking features are overlap-based: They tell us which parts of a why-question and its candidate answers are the most salient for ranking the answers.


In the case of why-QA, we know that we are not simply searching for information on dreaming but for an explanation for dreaming.

in the ranking process, we can extend the query with explanatory cue words such as because.


In the first example, this is (coincidentally) reflected by the corpus frequencies of the words in both clauses: mix (12,724) and together (83,677) have high corpus frequencies compared to baking (832), soda (1,620), vinegar (871), and explode (1,285).

the reference answer containing these terms is ranked in the top-10 by TF-IDF.


Lemmatization can be especially problematic in the case of proper names (which are not always recognizable by capitalization).

we decided only to lemmatize verbs (for features f4 and f8) in the current version of our system.


We considered using dynamic stemming for verb-noun relations such as the hibernation case but research has shown that stemming hurts as many queries as it helps (Bilotti, Katz, and Lin 2004).

we experimented with a number of different semantic resources, namely, the nominalization dictionary Nomlex (Meyers et al.


Most of the features that we implemented for answer re-ranking are based on word overlap between part of the question and part of the answer.

of this set-up, our features identify the parts of why-questions and their candidate answers that are the most powerful/effective for ranking the answers.


A substructure in the middle can be factorized by the composition of several left-and right-binarizations.

after a tree is parallel-binarized, to make the sub-phrases available to the MT system, all we need to do is to extract rules from the admissible nodes in the packed forest.


We think that these improvements are explained by the fact that tree re-structuring introduces more admissible trees nodes in the training trees and enables the forming of additional rules.


Each node in the directed graph is a candidate alignment and each edge denotes a transition between two nodes by adding a link.

that is we can remove most of the computational overhead by calculating only the difference of scores instead of the scores themselves.


In Figure 1, (1, 4), (4, 11), (5, 13), and (6, 14) are one-to-one links and the others are one-to-many links.

we introduce four features: Their feature gains cannot be calculated in a straightforward way because the addition of a link might change the link types of its siblings on both the source and target sides.


For example, if Zhongguo and China appear in an entry of a dictionary, they should be more likely to be aligned.

we use a single indicator feature to encourage linking word pairs that occur in a dictionary D: e, a, D e, a, D, j, i)  The system combination technique that integrates predictions from multiple systems proves to be effective in machine translation (Rosti, Matsoukas, and Schwartz 2007;He et al.


Therefore, we trained the IBM Models only on the development and test sets.

the AER scores in Table 6 look quite high.


However, this assumption results in a problem for our search algorithm that begins with an empty alignment (see Algorithm 1), for which φ 0 is J and the feature value h m 3 (f, e, a) is negative infinity.

to alleviate this problem, we modify Equation B.6 slightly by adding a smoothing parameter p n ∈ (0, 1): the feature gain for Model 3 is g m 3 (f, e, a, l) = log( g n 0 ( J, φ 0 )) + where f j and e i are linked by l, φ i is the fertility before adding l, and g n 0 ( J, φ 0 ) is the gain for n 0 (φ 0 | I i=1 φ i ): Model 4 defines a new distortion sub-model D(a) that relies on word classes A and B to capture movement of phrases.


For example, if a given Spanish sentence contains the phrase presidente de Brazil but the system does not have a translation for it, another Spanish phrase such as presidente brasileño may be automatically detected as a paraphrase of presidente de Brazil; then if the translation table contains a translation for the paraphrase, the system can use the same translation for the given phrase.

paraphrasing allows the translation system to properly handle phrases that it does not otherwise know how to translate.


Specifically, inducing the same paraphrase pair using an anchor pair that has already been seen only counts for 1 2 n , where n is the number of times the specific anchor pair has been seen so far.

induction of a path pair using new anchors is better evidence that the pair is paraphrastic, as opposed to the repeated induction of the path pair from the same anchor over and over again.


Using bilingual parallel corpora for paraphrasing has the inherent advantage that sentences in the other language are exactly semantically equivalent to sentences in the intended paraphrasing language.

the most common way to generate paraphrases with such a corpus exploits both its parallel and bilingual natures: Align phrases across the two languages and consider all co-aligned phrases in the intended language to be paraphrases.


The bilingual phrasal alignments can simply be generated by using the automatic techniques developed for the same task in the SMT literature.

arguably the most important factor affecting the performance of these techniques is usually the quality of the automatic bilingual phrasal (or word) alignment techniques.


Besides there being obvious linguistic differences between Arabic and English, the primary reason for the generation of phrase pairs that lie in categories (a) and (b) is incorrectly induced alignments between the English and Arabic words, and hence, phrases.

a good portion of subsequent work on paraphrasing using bilingual corpora, as discussed below focuses on using additional machinery or evidence to cope with the noisy alignment process.


Given that it is extremely unlikely that any such list will be exhaustive, any precision and recall measurements will not be accurate.


Though this level of agreement is low, naive baselines score even lower (see Section 5).

the metric does indeed distinguish human-like from baseline performance.


Therefore the metric does indeed distinguish human-like from baseline performance.

measuring one-to-one overlap with our annotations is a reasonable evaluation for computational models.


Pairs of utterances which are widely separated in the discourse are unlikely to be directly related-even if they are part of the same conversation, the link between them is probably a long chain of intervening utterances.

if we run our classifier on a pair of very distant utterances, we expect it to default to the majority class, which in this case will be different, and this will damage our performance in case the two are really part of the same conversation.


(For instance, two annotations have a sharp decline in score at about entropy 5.7, losing about 5% of performance with a change of just over .1 bit.)

it is not clear exactly how to choose a bias parameter which will yield good performance.


The objective function in Equation (1) allows any combination of trigrams to be selected.

invalid trigram sequences (e.g., two or more trigrams containing the "end" token) could appear in the target compression.


A simple way to enforce this is to give clauses weight proportional to the level of embedding (see the second term in Equation 9).

in Figure 1, the term l N is 1.0 (4/4) for clause S 4 , 0.75 (3/4) for clause S 3 , and so on.


This flexibility permits conceptually simple, highly expressive, and tightly coupled modeling of the relationship between the syntax and semantics of a language or the syntax and semantics of two languages.

it has frequently been put to use in a growing body of research into incorporating semantics into the Tree-Adjoining Grammar (TAG) framework (Kallmeyer and Joshi 2003;Han 2006;Nesson andShieber 2006, 2007).


Put simply, the trees in the tree sets used in our constructions meet the constraints of Tree Insertion Grammar (TIG), a known context-free-equivalent formalism (Schabes and Waters 1995).

we can conclude that the increase in complexity stems from the multi-component nature of the formalism rather than from the power added by an unconstrained adjunction operation.


The speed-up in the parser comes from traversing elementary trees bottom-up, handling only one link at a time.

no inference rule needs to maintain information about more than one link at a time.


Theorem 5 addresses the case in which the input string length is 0.

in this case, any factor in the complexity including the input string length cannot contribute to the overall time complexity.


Bounding the rank by a constant might be too strong a limitation in natural language parsing applications, however.

in the general case the running time of our algorithm contains a factor that is an exponential function of the rank of the input grammar.


For 1 ≤ j ≤ r, we write lnodes(n, l j ) to denote the set of all nodes from fragment α(n) with impinging link l j .


Each fragment that is excised from some source tree must obligatorily be adjoined back into that tree, at the point from which it was removed.

g generates the same derived trees as g, modulo our trivial tree transformation for the root and the gap nodes.


In the first case we have links(n 1 ) − links(n q ) < links(n 1 ) − links(n q ) and in the second case we have links(n ) < links(n 1 ) − links(n q ).

the smaller chain or fragment is processed earlier than our maximal chain, and by the time our maximal chain is processed, the smaller chain or fragment has already been excised.


Because n 3 dominates n 2 , n 3 dominates the nodes in lnodes(n 2 , j).

the only way σ(n 3 ) could equal 0 is if |lnodes(n 3 , j)| = |l j |.


Suppose the grammar in this example is modified as follows: Rules for lexical phrases are applied to X 0 These modifications exclude parses in which hierarchical translation rules generate other hierarchical rules, except at the 0 th level which generate lexical phrases.

the left most tree of Figure 10 cannot be generated and t 5 t 1 t 2 t 7 is the only allowable translation of s 1 s 2 s 3 s 4 .


Under the ITG constraint, three nodes {A l , A r , A p } are involved when we consider the order o between the two children {A l , A r } in any binary subtrees.

it is natural to define the ITG reordering P(r m ) as a function as follows: where o ∈ {straight, inverted}.


these two nodes are to be combined into a new node, whose target span is (1, 3).


Although BWR marginally outperforms LAR (32.47 vs. 32.17), simple boundary word features are not adequate to move phrases to appropriate positions because they cannot recognize syntactic contexts which are very relevant to phrase reordering.

the best way to reorder a phrase is to combine BWR and LAR so that we can use syntactic information on the one hand and not worry too much about syntactic divergences on the other hand.


The translation model proposes expansion candidates, and the query language model performs a selection in the context of the surrounding query terms.

in combination, the incessant problems of term ambiguity and query drift can be solved.


find for their "linguistic" systems in their Science paper.

as I argued in a contribution to Liberman (2009), one can "get a very good fit to [Rao et al.


It is clear that glamour and any deceit in the field of speech recognition blind the takers of funds as much as they blind the givers of funds.

we may pity workers whom we cannot respect (page 1049).


It lowered barriers to entry, by providing well-defined tasks and the resources needed to undertake them; it created a research community with shared goals and assumptions; and, perhaps most important, it offered proof of gradual progress that could be used to justify stable funding for speech and language technology development over several decades, even when no "killer app" had yet emerged.

of this demonstrated success, DARPA's investment in Human Language Technology continued, and grew to include text retrieval, information extraction, topic detection and tracking, summarization, machine translation, and many other problems.


I realized that one could consider such transition diagrams to be a form of pushdown-store automaton.

we could generalize finite-state transition diagrams to pushdown-store automata by allowing nonterminal symbols on transitions.


The two overlap considerably (Hirst 1981), but their definitions make them significantly different in essence: Paraphrasing concerns meaning, whereas coreference is about discourse referents.


Despite the differences, it is possible for paraphrasing and coreference to co-occur, as in the case of b and d. NLP components dealing with paraphrasing and coreference seem to have great potential to improve understanding and generation systems.

they have been the focus of a large amount of work in the past couple of decades (see the surveys by Androutsopoulos and Malakasiotis [2010], Madnani and Dorr [2010], Ng [2010], and Poesio and Versley [2009]).


If we understood "same meaning" in the strictest sense, a large number of paraphrases would be ruled out.

some authors argue for a looser definition of paraphrasing.


For this purpose, we introduce the null dependency structure e. For any operation OP and dependency structure X, Y, we have OP(X, Y) = e if this operation is not defined in Definition 5 OP(X, e) = X OP(e, X) = X Because part of a hypothesis may have a null dependency structure, we cannot calculate dependency LM scores on some of the related words.

we give a discount for each of these words.


The typical approach in the field has been a local one, in which each semantic task (or set of closely related tasks) is treated as a separate problem, that requires its own corpus-derived model and algorithm, both optimized to achieve the best performance in a given task, but lacking generality, since they resort to task-specific distributional representations, often complemented by additional taskspecific resources.

the landscape of DSMs looks more like a jigsaw puzzle in which different parts have been completed and the whole figure starts to emerge from the fragments, but it is not clear yet how to put everything together and compose a coherent picture.


Apparently unrelated semantic tasks can be addressed in terms of the same distributional memory, harvested only once from the source corpus.

thanks to the tensor-based representation, distributional data can be turned into a general purpose resource for semantic modeling.


In DM, matricization is applied to labeled tensors and it is the fundamental operation for turning the third-order tensor representing the weighted tuple structure into matrices whose row and column vector spaces correspond to the linguistic objects we want to study; that is, the outcome of matricization must be labeled matrices.

we must define an operation of labeled mode-n matricization.


A mode-n fiber is obtained by fixing the values of two indices, and by taking the tensor entries corresponding to the full range of values of the third index.

the natural choice for labeling a mode-n fiber is to use the pair formed by the labels of the two index elements that are fixed.


Similarly, for any row w 2 in the matrix resulting from mode-3 matricization, there will be an identical row w 1 in the mode-1 matricization.

given a weighted tuple structure T W extracted from a corpus and subject to the constraints we just mentioned, by matricizing the corresponding labeled third-order tensor X λ we obtain the following four distinct semantic vector spaces: Words like marine and teacher are represented in the W 1 ×LW 2 space by vectors whose dimensions correspond to features such as use, gun or own, book .


), whereas the DM representation of distributional data as weighted triples is more akin to other standard knowledge representation formats based on typed relations, which are familiar to most computer and cognitive scientists.

a trained DM can become a general-purpose resource and be used by researchers beyond the realms of the NLP community, whereas applying PairClass requires a good understanding of various aspects of computational linguistics.


For the DepDM model, the only attested links are n with q (Constitutive), n sbj intr q, n sbj tr q (Telic), and q obj n (Agentive).

depdM does not harvest Formal qualia, and is penalized accordingly in the evaluation.


For instance, the transitive subject of unaccusative (The man broke the vase) and unergative verbs (The jockey raced the horse past the barn) is an agent of causation, whereas the subject of the intransitive variant of unaccusative verbs has a theme role (i.e., undergoes a change of state: The vase broke), and the intransitive subject of unergative verbs has instead an agent role (The horse raced past the barn).

their surface identity notwithstanding, the semantic properties of the syntactic slots of the verbs in each class are very different.


However, it is also notoriously prone to over-estimating the importance of rare items (Manning and Schütze 1999, Chapter 5).

before selecting the top 20 links ranked by PMI, we filter out those links that do not have at least 10 non-zero dimensions in the positive subspace.


Block entropy for block size N is defined as: are the probabilities of sequences (blocks) of N symbols.

for N = 1, block entropy is simply the standard unigram entropy and for N = 2, it is the entropy of bigrams.


A good example is from Egyptian (Allen 2000), where there is a hierarchy that determines the order in which terms denoting human or divine entities are written.

terms for gods will occur in writing before terms for humans or other things, even if the divine terms would be spoken after the other terms.


The most obvious deficiency in ROUGE is the lack of a linguistic quality measurement, which we take to encompass all language-related issues: lexical, syntactic, and semantic.

we conjecture that most remaining prediction error in Nouveau-ROUGE is a result of omitting linguistic quality and caution that better prediction would be achieved only for systems of comparable linguistic quality.


A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process.

the method is semi-supervised due to the use of opinion word seeds.


A word can be positive in one domain but has no opinion or even negative opinion in another domain.

it is necessary to expand a known opinion lexicon for applications in different domains using text corpora from the corresponding domains.


Our approach differs from existing approaches in that it requires no additional resources except an initial seed opinion lexicon, which is readily available.

it can be seen as a semi-supervised method due to the use of the seeds.


The dependency relations describing relations between opinion words and targets include mod, pnmod, subj, s, obj, obj2 and desc; and the relations for opinion words and targets themselves contain only the conjunction relation conj.

we formulate OT-Rel, OO-Rel, or TT-Rel as a quadruple POS(w i ), DT, R, POS(w j ) , in which POS(w i ) is the POS tag of word w i , DT is the dependency type (i.e., DD or IDD), and R is the syntactic relation.


Polarities of opinion words are important for many opinion mining tasks.

the newly extracted opinion words should be assigned with polarities.


Note that targets themselves convey no polarities and opinion words are the only expressions that people use to show their attitudes towards targets.

the polarities of targets inherit those of associated opinion words.


We also observe that these words can cancel the polarity change when they are used together or associated with negations.

we consider that the polarity changes only when there is an odd number of such contrary words and negations between the two opinion words or targets.


To train CRF for the extraction task, we use one data set for training and the remaining four sets for testing.


In the 10p case, the new opinion words extracted by our approach could cover almost 75% of the whole opinion set whereas the corresponding seed words only cover 8% of the opinion words in the data (see the Init line).

our propagation method is quite powerful in identifying a large number of new opinion words.


It is not suitable for the fine-grained extraction task.

a large number of non-targets are included.


A wide-coverage TAG may contain hundreds or even thousands of elementary trees, and syntactic structure can be redundantly repeated in many of them (XTAG Research Group 2001;Abeillé, Candito, and Kinyon 2000).

maintenance and extension of such grammars is a complex task.


This casting is not part of the core of a signature, but rather a device that enables advanced module communication.

we define signature modules in two steps.


A signature module is extended to a type signature after all the information from the different modules have been gathered.

there is no need to preserve the classification of nodes and only the underlying PSS is of interest.


When TFSS are defined over signature modules this is not the case, because signature modules may include anonymous nodes.

the standard definition of TFSS is modified such that every path in a TFS is assigned a node in the signature module over which it is defined, rather than a type.


In human-computer dialogue, Swerts and Ostendorf (1995) found that the first utterance of a discourse segment correlates with slower speaking rate and longer preceding pause.

we are interested in whether discourse markers and prosodic cues are also used in signaling task interruptions in multi-tasking dialogue.


In this research, we analyze the last two conversations, but not the practice one.

we have a total of about 180 minutes of conversation from the six pairs of players.


The game, card, and picture segments are cohesive units of discourse in which the conversants attempt to complete a domain task, that of winning the card game, deciding what card to discard, or identifying a picture.

they follow Grosz and Sidner's (1986) definition of discourse segments.


For example, the duration and energy of bat are usually larger than bit.

a large amount of data are required to balance out these differences.


wait is used to mark a discontinuity in the ongoing topic, which is also required by task switching.

it seems natural for people to use these discourse markers to signal switching to a real-time task.


Due to our task setup, players tend to mutually know when a picture game ends.

we do not examine how players signal task resumption, but instead we focus on how players restore the context of the ongoing task, that is, how players re-establish the conversation on poker playing after being interrupted by a picture game.


However, the version here differs as it includes the new card just picked up, which has not been communicated before.

we refer to this act as card review + new card.


For comparison, we use a naive baseline that assumes that all cases of do you have are task interruptions, which has an accuracy of 59.6%.

we achieve 54.0% relative error reduction in comparison to the baseline.


The DYH down-sampling, however, still has imbalanced do you have utterances in the two classes.

we further introduce the Balanced-DYH down-sampling, in which we use all 105 do you have utterances in the poker playing and 38 other (i.e., non do you have) utterances in task interruptions, and randomly select 105 do you have utterances from task interruptions and 38 other utterances from poker playing.


Sussman, Winkler, and Schröger (2003) found that higher pitch can serve as a more intrusive signal.

when interrupting in the middle of a card discussion, the speaker uses higher pitch probably because the hearer is more engaged in the ongoing task.


At the ith step (0 < i ≤ K), a state item candidate i is generated by extending an existing state item candidate i−1 on the agenda with δ(candidate i , i).

in this case, we have when a state item is extended, its score can be updated by adding the incremental score of the step Φ(δ(candidate i , i)) • w. The nature of the scoring function means that, given appropriately defined features, it can be computed efficiently for both the incremental decoding and training processes.


For example, could be treated as a single word (Bank of Beijing) for machine translation, although it is more naturally segmented into (Beijing) (bank) for tasks such as text-to-speech synthesis.

supervised learning with specifically defined training data has become the dominant approach.


However, because the largest-range feature templates span only over two words (see Table 1), the highest scored among the segmentations of characters 0..b − 1 with the last word being characters b ..b − 1 will also give the highest score when combined with the word b..n − 1.

the highest-scored segmentation with the last word being characters b..n − 1 can be found as long as the highest-scored segmentations of 0..b − 1 with the last word being b ..b − 1 are found, where b ∈ 0..b − 1.


When this happens, we choose to retain fully built outputs in the agenda while continuing the decoding process, until the highest scored item in the agenda is a complete parse.

there are situations when different parses in the agenda have a different number of actions.


The database used in the WOZ experiments contained over a million songs and is searched with partial string match.

the retrieved items for 'Layla' can be as high as 142.


Nevertheless, as observed by Buchholz and Marsi (2006), it is striking that recent work on data-driven dependency parsing has been dominated by global, exhaustive, graph-based models, on the one hand, and local, greedy, transition-based models, on the other.

a careful comparative analysis of these model types appears highly relevant, and this is what we will try to provide in this article.


Our work differs from past studies in that we attempt to quantify exactly the types of errors these parsers make, tie them to their theoretical expectations, and show that integrating graph-based and transition-based parsers not only increases overall accuracy, but does so directly exploiting the strengths of each system.

this is the first large-scale error analysis of modern data-driven dependency parsers.


On the other hand, if two arcs (i, j, l) and (k, m, l ) do not overlap, then the leftmost arc has to be added first (because of arc-eagerness).

we can expect error propagation from shorter to longer overlapping arcs and from preceding to succeeding arcs.


By contrast, MSTParser is forced to restrict the scope of features to a single or pair of nearby parsing decisions in order to make exhaustive inference tractable.

the feature representation available to the locally trained greedy models is much richer than the globally trained exhaustive models.


The first-stage learns a model to predict unlabeled dependency trees for a sentence.

arc scores do not condition on possible labels and are parameterized by features only over the head-modifier pair, s(i, j) = w • f(i, j).


System performance is almost indistinguishable, but MaltParser tends to perform better on shorter sentences, which require the greedy inference algorithm to make fewer parsing decisions.

the chance of error propagation is reduced significantly when parsing these sentences.


In ambiguous situations, it is not surprising that MSTParser predicts many length-1 dependencies, as this is the most common dependency length across treebanks.

whereas MaltParser pushes difficult parsing decisions higher in the graph, MSTParser appears to push these decisions lower.


Now, define a new training set of the form .

that is, t is identical to t, except that each training input x is augmented with the cross-validation prediction of model C. Finally, let this means that, for every sentence x ∈ t, B C has access at training time to both the gold standard dependency graph G x and the graph G C x predicted by C. B C is able to define guide features over G C x , which can prove beneficial if features over G C x can be used to discern when parsing model C outperforms or underperforms parsing model B.


For the guided graph-based model, which we call MST Malt , this feature representation is modified to include an additional argument G Malt x , which is the dependency graph predicted by MaltParser on the input sentence x.

the new feature representation will map an arc and the entire predicted MaltParser graph to a high dimensional feature representation, f(i, j, l, G Malt x ) ∈ R k+m .


However, except for the two extreme data points (0-10 and 51-60) there is also a slight tendency for Malt MST to improve more for longer sentences (relative to its base model) and for MST Malt to improve more for short sentences (relative to its base model).

whereas most of the improvement for the guided parsers seems to come from a higher accuracy in predicting arcs in general, there is also some evidence that the feature-based integration allows one parser to exploit the strength of the other.


This suggests that the improvement is not a simple function of the raw accuracy of the guide model but depends on the fact that labeled dependency decisions interact in inference algorithms for both graph-based and transition-based parsing systems.

if a parser can improve its accuracy on one class of dependencies (for example, longer ones), then we can expect to see improvements on all types of dependencies-as we do.


We constructed the new tree decomposition by adding nodes of size 3.

as long as the treewidth of G was at least 3, tw(G ) ≤ tw(G).


In the other direction, because G is a subgraph of G , any tree decomposition of G forms a valid tree decomposition of G after removing the vertices in G − G, and hence tw(G ) ≥ tw(G).

tw(G ) = tw(G) Because every vertex in G has even degree, G has an Eulerian tour, that is, a path visiting every edge exactly once, beginning and ending at the same vertex.


Although a polynomial time algorithm for this problem would not necessarily imply that P = NP, it would require progress on fundamental, well-studied problems in graph theory.

it does not seem possible to exploit the special structure of graphs derived from LCFRS productions.


This principle of tolerance gives rise to a puzzling situation: If I keep on turning down the dimmer, small step by small step, the diminution in brightness will be imperceptible at each step.

if the light is bright at step n, then tolerance dictates that it will also be bright at step n + 1.


For the more inflected Slavic and Romance languages considered in this book, the tag sets include as many as 4,000 tags, each marking a full suite of morphosyntactic features, such as tense, case, or number.

in linguistic terms, morphosyntactic tagging is exactly contextually disambiguated morphological analysis.


Here, adjectives such as total do not have a semantic orientation of their own, but, just like adverbial intensifiers, contribute to the interpretation of the word that follows them; total failure is presumably worse than just failure.

we have a separate dictionary for adjectival intensifiers.


Instead of changing the sign, the SO value is shifted toward the opposite polarity by a fixed amount (in our current implementation, 4).

a +2 adjective is negated to a −2, but the negation of a −3 adjective (for instance, sleazy) is only slightly positive, an effect we could call "damning with faint praise."


In the current version of SO-CAL, we have used a somewhat different approach, instead supposing that negative expressions, being relatively rare, are given more cognitive weight when they do appear.

we increase the final SO of any negative expression (after other modifiers have applied) by a fixed amount (currently 50%).


The annotation is done on a 1 to 5 scale, where 1 indicates "no emotion."

we focused on the comments with scores of 4 and 5.


The second method used in evaluating SO-CAL on SO-empty texts is to only classify texts for which it has direct evidence to make a judgment.

we exclude such SO-empty texts from the evaluation.


This is part of a research program which, just like that of Harris, aims at eliminating semantics and considerations of meaning completely from the process of "discovery" of language structure.

andreev claims to be able to go from unsegmented transcribed speech all the way up to syntax, using basically one and the same approach grounded in text (corpus) statistics.


Entropy would also take into account the skewedness of -o-versus -e-, whereas for Hammarström (2009b) and Golcher (2006) only the skewedness of the most skewed character (i.e., the character that potentially constitutes the morpheme continuation) is interesting, in this example -i-.

these approaches only use the maximally skewed character to predict the presence/absence of a morpheme boundary.


Indeed, any frequent character at the end or beginning of a word may also be expected to show high LSV/LSE/LSM around it, such as the -e at the end of disturbance which has higher values than, for example, -ance.

simply inferring that high LSV/LSE/LSM values indicate a morpheme border is not a sound principle in general.


This can be useful because in SVM-rank the interpretation of C is slightly different than in standard SVM, specifically C svm = C rank /m, where m is the number of queries, or questions in our case.

an optimal value can depend crucially on the target data.


Throughout the book, type-logical grammar is motivated via English examples and those English examples are not English utterances drawn from books or newspapers but rather carefully constructed examples such as those commonly found in mainstream linguistics research.

the arguments made often have little relevance to mainstream computational linguistics.


(2010) show that standard machine learning techniques (in their case, a naive Bayes classifier) can outperfom the Turkers on a categorization task (classifying businesses into Automotive, Health, Real Estate, etc.).

in some cases, NLP tools already do better than MTurk.


MTurk costs will then become the standard costs, and it will be very difficult to obtain funding for a project involving linguistic resource production at any level that would allow for more traditional, non-crowdsourced resource construction methodologies.

our community's use of MTurk not only supports a workplace model that is unfair and open to abuses of a variety of sorts, but also creates a de facto standard for the development of linguistic resources that may have long-term funding consequences.


IV and LRV indicate certainty and uncertainty of co-occurrence, respectively.

cV is the combination of certainty and uncertainty.


We adopt a dynamic programming technique to limit the computational complexity of the algorithm in Selection.

the algorithm takes polynomial time over the length of the character sequence being processed.


After Selection, one of the ABCs is selected as a word but not the other.

the frequencies of all proper subsequences of the selected ABC are reduced by 1 and those of the other ABC are not changed, as shown in Figure 7.


Because of the idea of Adjustment, the Fs of these subsequences need to be reduced.

the records become 1, and BBC(1) after Adjustment.


The result in the hierarchical format cannot directly be evaluated by the Bakeoff score script.

the hierarchical format will be changed to a format that looks like A B C, where the symbol denotes a space character.


Therefore, F is the only variable and E can be viewed as a function of F, that is, Both C and N are constant; meanwhile, C, N, and F are positive integers.

e is a monotone increasing function of F. For an input character sequence of finite length, the segmentation results produced by eSA converge.


For example, the complexity of Nested Pitman-Yor Language Model (NPYLM) (Mochihashi, Yamada, and Ueda 2009) is O(N×L 2 ) for bigrams, where N is the length of a character sequence and L is the maximum word length accepted.

the time complexity of NPYLM is O(N 3 ) when the length of the character sequence and the maximum word length are equal to each other.


The approach calculates the escape probabilities and the probabilities of successive characters according to the training corpus that is manually segmented.


The DLG value is negative when replacing the one-character sequence.

dLG cannot uniformly process words of different lengths.


In addition, the "total vote" is the strategy used to combine the two kinds of information.

the similarities and differences between ESA and TONGO are similar to those between ESA and IWSLRR.


Zhao and Kit (2008a) tested AV, BE, and DLG with both the training and test data of Bakeoff-3 to extract word candidates.


The window size of VE was six and the punctuation of the corpus was removed in their experiment.

we use the result of setting 3 to compare with VE, as shown in Table 10.


The results showed that mixing randomly generated in-domain utterances with rule-based in-domain utterances improves performance; the rule-based utterances provide a way to incorporate knowledge from the personality psychology literature into an SNLG approach.

personality scoring models can be effective, although the computational cost of the OS approach remains a major drawback.


A consequence is that non-deterministic decision points are introduced to satisfy these naturalness constraints (e.g., if too many pragmatic marker parameters are enabled, only a random subset will appear in the utterance).

the only assumption we make regarding the impact of parameter values on the generation process is that they affect the likelihood of observing their intended effect over a large set of utterances.


To limit the experiment's duration, only the two traits with extreme target values are evaluated for each utterance.

20 utterances are evaluated for each trait, 10 of which were generated to convey the low end of that trait, and 10 of which target the high end of that trait.


It is important to emphasize that generation parameter values were predicted based on five target personality scores.

the results show that individual traits are perceived even when utterances project other traits as well, as would be expected according to the Big Five theory.


Moreover, the DARPA Communicator evaluation showed that task duration is negatively correlated with user satisfaction (r = −0.31, p < 0.001, see Walker, Passonneau, and Boland [2001]).

there is reason to believe that improvements in information presentation will lead to improvements in spoken dialog systems.


In addition, we introduce a content structuring algorithm that supports stepwise refinement, as in Polifroni, Chung, and Seneff (2003), but in which the structuring reflects the user's preferences.

our approach maintains the benefits of user tailoring, while also being capable of dealing with a large number of options.


For example, the justification of the flights in the left branch of the tree in Figure 8 is their fare class.

they are described as flights with availability in business class.


Because UMSR employs information from a user model and content structuring to present only those options that are likely to be relevant to the user, and explicitly points out trade-offs among options, we hypothesized that it would also consume less cognitive resources, that is, presentations based on UMSR would impose less cognitive load than those based on SR.

in parallel to our work on the interaction task experiment, a companion project conducted experiments using a dual-task setting (driving a simulated car while using the dialog system to book flights) to investigate the effects of the two strategies to information presentation (UMSR vs. SR) on cognitive load (Hu et al.


The fundamental structures in dependency parsing are dependency trees.

just as items for constituency parsers encode sets of partial constituency trees, items for dependency parsers can be defined using partial dependency trees.


This implementation has the same tree building logic as the parser described by Nivre (2003), differing from it only with respect to the control structure.

it can be seen as a different realization of the schema shown in Section 3.6.


Because these three items have a length strictly less than l, by the inductive hypothesis, they are derivable.

the item [i, k, i] is also derivable, as it can be obtained from these derivable items by the following inferences: This proves that all coherent items of length l which are of the form [i, k, i] are derivable under the induction hypothesis.


These extra actions allow the parser to link to nodes that are several positions deep in the stack, creating non-projective links.

in particular, Attardi uses six non-projective actions: two actions to link to nodes that are two positions deep, another two actions for nodes that are three positions deep, and a third pair of actions that generalizes the previous ones to n positions deep for any n. the maximum depth in the stack to which links can be created can be configured according to the actions allowed.


For finite values of k, MH d+2 is a static filter of Att d , because its sets of items and deduction steps are subsets of those of Att d .

the set of structures parsed by MH d+2 is also a subset of those parsed by Att d .


The COMBINE steps perform a union operation between subtrees.

the result is a dependency tree containing all the dependent subtrees, and with all of them linked to h-this is the subtree induced by h. This process is applied repeatedly to build larger subtrees, until, if the parsing process is successful, a final item is found containing a dependency tree for the complete sentence.


As WG k is a parser for well-nested structures of gap degree up to k, its COMBINER steps correspond to all the ways in which we can join two sets of sibling subtrees meeting these constraints, and having a common head, into another.

this parser does not use COMBINER steps that produce interleaved subtrees, because these would generate items corresponding to ill-nested structures.


For example, the COMBINE INTERLEAVING GAP C step in MG 1 is obtained from the string abgab.

we define the parsing schema for MG k , a parser for mildly illnested structures for gap degree k, as the schema where the item set is the same as that of WG k , except that items now contain mildly ill-nested structures for gap degree k; and the set of deduction steps consists of the LINK step in WG k , plus a set of COMBINE steps obtained as explained herein.


None of the treebanks for nine different languages 10 contain structures that are strongly ill-nested for their gap degree (Table 1).

in any of these treebanks, the MG k parser can parse every sentence with gap degree at most k in time O(n 3k+4 ).


Finally, we observe that that some well-known parsing algorithms discussed here (Nivre 2003;) rely on statistically-driven control mechanisms that fall below the abstraction level of parsing schemata.

it would be useful to have an extension of parsing schemata allowing the description and comparison of these control structures in a general way.


A straightforward method for cross-lingual sentiment classification is to use machine translation for transferring lexica or corpora of reviews between English and Chinese, and then apply the lexicon-based or corpus-based method for sentiment classification in either the English or Chinese language.

the basic methods consist of two main steps: resource translation and sentiment classification.


3 The labeled English reviews are translated into labeled Chinese reviews and the unlabeled Chinese reviews are translated into unlabeled English reviews by using machine translation services.

each review is associated with an English version and a Chinese version.


In the training phase, the co-training algorithm learns two separate classifiers: C en and C cn .

in the classification phase, we can obtain two prediction values for a test review.


NLP researchers have become increasingly interested in unsupervised and minimally supervised methods, and the rise of probabilistic models of cognition (Chater and Oaksford 1998;Griffiths, Kemp, and Tenenbaum 2008) means there is now a growing number of cognitive scientists who are well-versed in many of the same statistical methods that are used in NLP.

a short introductory text on computational modeling of human language acquisition seems particularly apt at this time.


This clearly poses serious challenges in terms of computation for traditional text processing approaches using a single computer.

efficient distributed computing has become more crucial than ever.


However, I am never completely happy with my work, as it seems a pale shadow of what I think it should have been.

i am picking a different model-things we all know but do not say out loud because we have no evidence to support them; and besides, making such bold claims sounds pretentious.


Thus I am picking a different model-things we all know but do not say out loud because we have no evidence to support them; and besides, making such bold claims sounds pretentious.

i do not expect to say anything too novel here.


Nobody reads them the Penn Treebank or any other training data early in their career.

generative models seem to be the only game in town.


Of course, the maximum likelihood distribution is called the maximum likelihood distribution because for a given set of counts it assigns probabilities to make the probability of the data (the "likelihood") as high as possible.

it will lead us to the memorization result.


Playing in the Penn Treebank has the part of speech VBG, for progressive verb, and the s in plays indicates a present-tense third-person-singular verb (VBZ).

the tag induction program in Clark (2003) actually computes the joint probability of the part of speech and the morphology.


The problem with standard PER is that it is not possible to detect which words are deletion errors, which are insertion errors, and which words are substitution errors.

we introduce alternative PER-based measures which correspond to the precision, recall, and F-measure.


Differences of word order in the hypothesis with respect to the reference are taken into account only by WER and not by PER.

a word which occurs both in the reference and in the hypothesis but is marked as a WER error is considered as a reordering error.


Both coefficients assess how well a monotonic function describes the relationship between two variables: The Pearson correlation assumes a linear relationship between the variables, and the Spearman correlation takes only rank into account.

spearman's Table 5 Examples of two variants of human error analysis, a strict and a flexible one; the marked errors are detected with respect to the reference, whereas no errors are detected when the error analysis is more flexible.


Nevertheless, these reordering errors are not at all detected by automatic error analysis-it detects reordering errors only if exactly the same words in the reference and in the hypothesis are present.

there are more discrepancies between the human and automatic error analysis for all three outputs: called off is detected as missing, disappeared as a lexical error, and in the hierarchical system output have been are considered as extra words.


We do not omit the extra words because this category is informative for the distribution of errors in a particular translation output.

the following novel metrics are defined: Number of RPER (reference PER) or HPER (hypothesis PER) errors caused by wrong choice of the full word form normalized over the (closest) reference length.


Both points are problematic not only for a statistical review such as that conducted in this article, but also for a fair appreciation of agreement figures in reports, as will be discussed subsequently.

of these observations and based on the results of our meta-analysis, we developed what we consider a minimal set of information that should be reported (see Section 5.2).


We used the individual agreement rates in the meta-analysis, not combined rates on study-level.

for some studies more than one agreement rate was included in the analysis when they differed with regard to the moderator variables scrutinized here.


5 A small group of annotators is unlikely to fully represent and explore this space of possible annotations-especially when more complex material and annotation schemes are used.

the confidence for a judgment on the trustworthiness of annotations increases with the number of coders.


Although we cannot rule out that the specific statistical properties of agreement indices may introduce biases into the statistical results, we are confident that these influences will not change the general picture of our results, especially given the large sample and the robust effects we found for most factors.

although different agreement indices are based on slightly different concepts of agreement, the general intent and direction of indices are comparable.


We therefore argue that the results in this article are generalizable to other domains, as long as these domains do not differ more from the studies in our sample than these studies differ among themselves.

if the reader considers these three domains as typical for linguistic annotation tasks the results of the overall analysis can be seen as generic.


In the second sentence everything that the spokesperson (here the police) has said is compiled into a single sentence by the reporter, and it is unlikely that the spokesperson said only a single sentence with all this information.

it is reasonable to give longer duration to this "said" event.


The overall agreement is relatively low.

in this article, we mainly focus on learning coarse-grained event durations with much higher inter-annotator agreement.


The distribution of the means of the annotated durations in Figure 3 is bimodal, dividing the events into those that take less than a day and those that take a day or more.

in our first machine learning experiment, we have tried to learn this coarse-grained event duration information as a binary classification task.


Whereas before it was difficult to even evaluate what NP information was being recovered, we have set a high benchmark for NP structure accuracy, and opened the field for even greater improvement in the future.

downstream applications can now take advantage of the crucial information present in NPs.


The incorrect NML and JJP nodes are hidden by incorrect NP or ADJP nodes and the difference cannot be recovered.

the NML and JJP brackets difference in Table 7 is for recall, not F-score.


Our eventual aim, however, will be to build a post-processor that can bracket the full range of NPs in the Penn Treebank.

in our later experiments we will take on the much more difficult task of bracketing complex NPs from our newly annotated corpus.


A widely used Collins-style parser, together with our NP post-processor, and trained using the extended corpus we created, is now able to identify sub-NP brackets with a level of accuracy that can be exploited by many practical applications.

we have made it possible to increase performance on question answering, anaphora resolution, and many other downstream NLP tasks.


The labeling rests on the assumption that the people who produced the summaries used their own prior knowledge in choosing appropriate references for the summary.

they could refer to people they regarded as familiar to the general public using short forms such as (1) title or role + surname or (2) full name only with no pre-or post-modification.


The hearer-new/hearer-old distinction is dynamic; when initially unfamiliar characters (like Saddam Hussein before the first Gulf War) appear in the news over a period of time, they can become hearer-old.

the classification of the same person can be different for different document sets dating to different years.


The important features were: the number of appositions (features 7, 8) and relative clauses (feature 9), number of mentions within the document set (features 0,1), total number of apposition, relative clauses and copula (feature 12), number of high frequency pre-modifiers (feature 18), and the minimum bigram probability (feature 17).

the lexical and syntactic features were more useful than frequency features for determining familiarity.


Their estimates rely on contexts in the training data that are similar to (or in the same class as) the new sequence whose probability is to be estimated.

for example, in attempting to estimate a probability for the bigram black cloud, unseen in training, the transition probability associated with the class to which black belongs being followed by the class to which cloud belongs can be used.


In evaluating our model it seems appropriate to compare its performance against other class-based models.

the SRI toolkit was also used to construct a class bigram language model, following the incremental version of the algorithm proposed by Brown et al.


However, for a number of strata (6-9, 10, 12) P (KN-Half) only uses one context for prediction, which in many cases is an inferior choice compared to the predicting contexts used by P (KN) .

the averages in these strata are negative.


These bigrams have similar right contexts, but dissimilar left contexts.

the whole-context model groups underwriter was with other bigrams that do not support good generalization.


First, the discounting mechanism defined in Equation (1) varies the weight that class-based generalization is given: Weights for unseen and infrequent events are higher than weights for frequent events.

the model's estimates are close to maximum likelihood estimates for frequent events because the maximum likelihood estimator is appropriate in these cases.


This number scales linearly with the number of distinct trigrams, which in turn scales sublinearly with the length of the training corpus.

although the estimation procedure is expensive compared to standard trigram models like KN, it has desirable properties compared to other clustering algorithms, in particular the exchange algorithm.


In particular, items are sets of partial constituency trees that are licensed by rules of the grammar.

parsing schemas allow compilation of schemas to executable parsers and also permit formal reasoning about properties of the parser directly.


All features related to absolute (i.e., unnormalized) pitch values, such as maximum pitch or final pitch slope, are not comparable across genders because of the different pitch ranges of female and male speakers-roughly 75-500 kHz and 50-300 kHz, respectively.

before computing those features we applied a linear transformation to the pitch track values, thus making the pitch range of speakers of both genders approximately equivalent.


Note that these two functions are relatively simple: CBeg calls for the listener's attention, and is frequently conveyed with a filled pause (uh, um); Chk asks the interlocutor for confirmation, which may alternatively be achieved via a high-rising intonation.

it is not surprising that these functions take less time to be realized than other more pragmatically loaded functions, such as agreement.


Of the 6,700 candidate turns in the corpus, only 2,351 (35%) begin with an isolated ACW, including 753 of the 757 backchannels in the corpus.

12 an on-line classification procedure would only need to identify backchannels in those 2,351 turns.


a n for a i ∈ A, and we defineˆ = 1, where is the empty string.

the mapping defined byˆallows us to associate an element of the algebra with every string of words.


We use this to define the mapping ξ from terms to vectors.

in this example, we have ξ(cat) = (0, 2, 3) and ξ(big) = (1, 3, 0).


The finite-dimensional real vector spaces R n can be considered as special cases of the sequence spaces (consisting of vectors in which all but n components are zero) and, because they are finite-dimensional, we can use any of the p norms.

our previous examples, in which ξ mapped terms to vectors in R n , can be considered as mapping to abstract Lebesgue spaces if we adopt the 1 norm.


For example, WordNet specifies that there is an entailment relation between the predicates pay and buy, but does not describe the way in which arguments are mapped: if X pays Y for Z then X buys Z from Y.

using WordNet directly to derive entailment rules between predicates is possible only for semantic relations such as hyponymy and synonymy, where arguments typically preserve their syntactic positions on both sides of the rule.


Whereas troponomy rules (Fellbaum 1998a) such as X walks → X moves can be perceived as being almost always correct, rules such as X coughs → X is sick might only be true with some probability.

chaining a few probabilistic rules such as A → B, B → C, and C → D might not guarantee the correctness of A → D. Because in focused entailment graphs the number of nodes and diameter 4 are quite small (for example, in the data set we present in Section 5 the maximal number of nodes is 26, the average number of nodes is 22.04, the maximal diameter is 5, and the average diameter is 2.44), we do not find this to be a problem in our experiments in practice.


We can phrase it as a decision problem in the following manner: Given V, f , and a threshold k, we wish to know if there is a set of edges E that respects transitivity and Yannakakis (1978) has shown that the simpler problem of finding in a graph G = (V , E ) a subset of edges A ⊆ E that respects transitivity and |A| ≥ k is NP-hard.

we can conclude that our optimization problem is also NP-hard by the trivial polynomial reduction defining the function f that assigns the score 0 for node pairs (u, v) / ∈ E and the score 1 for node pairs (u, v) ∈ E .


Because the decision problem is NP-hard, it is clear that the corresponding maximization problem is also NP-hard.

obtaining a solution using ILP is quite reasonable and in our experiments also proves to be efficient (Section 5).


In Appendix A we specify some simplifying independence assumptions under which this graph maximizes the following linear objective function: where η = P(I uv =1) P(I uv =0) is the prior odds ratio for an edge in the graph, which needs to be estimated in some manner.

the weighting function is defined by f prob (u, v) = log P uv 1−P uv + log η.


We would like to use the score S uv , which is bounded in (∞, −∞), and derive from it a probability P uv .

to that end we project S uv onto (0, 1) using the sigmoid function, and define P uv in the following manner: Note that under this definition the log probability ratio is equal to the inverse of the sigmoid function: when we derive P uv from S uv with the sigmoid function, we can rewritê G prob as:Ĝ where we see that in this scenario the two objective functions are identical and the regularization term λ is related to the edge prior odds ratio by: λ = − log η.


Twelve distributional similarity measures were computed over the health-care corpus using the aforementioned variations (Section 4.1), where two feature representations were considered: in the UMLS each natural language phrase may be mapped not to a single CUI, but to a tuple of CUIs.

in the first representation, each feature vector coordinate counts the number of times a tuple of CUIs was mapped to the term instantiating the template argument, and in the second representation it counts the number of times each single CUI was one of the CUIs mapped to the term instantiating the template argument.


We note that in the experiments reported in this article the optimal solution without integer restrictions was already integer.

although in general our optimization problem is NP-hard, in our experiments we were able to reach an optimal solution for the input graphs very efficiently (we note that in some scenarios not reported in this article the optimal solution was not integer and so an integer solution is not guaranteed a priori).


This can be explained because our algorithm is not purely probabilistic: The learned graphs are the product of mixing a probabilistic objective function with non-probabilistic constraints.

plugging the estimated prior into this model results in performance that is far from optimal.


First, the strongly connected component can no longer be untied.

in A3 we observe that in future iterations the strongly connected component expands further and many more wrong edges are inserted into the graph.


Because in ILP-Global the strongly connected component was split in two, it is possible to connect these two nodes to some of the other nodes and raise the recall of ILP-Global.

we see that greedy optimization might get stuck in local maxima and consequently suffer in terms of both precision and recall.


Notice that in step A2 the templates X causes headache and X results in headache are already included (erroneously) in a connected component with the templates X prevents headache and X reduces headache.

adding the rules from Associate X with headache and Associate headache with X to X causes headache and X results in headache would also add the rules to X reduces headache and X prevents headache and the Greedy-Global avoids that.


In this section we would like to examine the utility of various features, while avoiding the biases that occur due to distant supervision.

we use the 23 manually annotated gold-standard graphs for both training and testing, in a cross-validation setting.


We would like to extract features that express information that is diverse and orthogonal to the one given by distributional similarity.

we turn to existing knowledge resources that were created using both manual and automatic methods, expressing various types of linguistic and statistical information that is relevant for entailment prediction: WordNet: contains manually annotated relations such as hypernymy, synonymy, antonymy, derivation, and entailment.


In addition, we would like to test whether using different classifiers affects performance.

we run the experiments with a linear-kernel SVM, a square-kernel SVM, a Gaussian-kernel SVM, logistic regression, and naive Bayes.


r Looking at the training set accuracy, ablations, and precision of the new features, it seems that the behavior of most of them is reasonable.

it is possible that in a different learning scheme that does not use the resources as features the information they provide may become beneficial.


Typically, S and SS can be identified by descriptions, with S distinguished by an extra attribute.

in our A-level example, SS is the set of A-level papers marked in 2008, and S is the subset of these papers that received an A grade.


Another clue, it has been argued, is provided by the given value itself, because round numbers are likely to be interpreted as approximations (Krifka 2002).

in "a quarter of papers were marked A" the given value is a simple fraction, suggesting that V G has been selected by the writer as a round number conveniently near V A , and that the relationship is accordingly The term hedge was introduced by Lakoff (1973) and subsequently applied to numerical descriptions by Dubois (1987), who studied imprecision in oral scientific presentations and listed some common numerical hedges (e.g., "about", "almost", "nearly", "of the order of", "a little over"); Crystal (1988) added a few more.


The first crucial insight from linguistic work on hedging is that proportions (and indeed other quantities) are expressed by relating them to another, typically different quantity that we have called the given value V G , with the hedge (if present) indicating the nature of this relationship.

in expressing the proportion 0.259 by "over a quarter," we have an actual value V A (0.259), a given value V G (1/4), and a relationship V A > V G .


To specify a given value V G we must choose (a) a system, (b) a scale from the system, and (c) a point from the scale-the points on a scale S N being represented by integers from 0 to N. This formalization of the given value has the advantage of distinguishing not only degrees of roundness, but also different mathematical forms, which can be equated with scale systems.

the system including [S 10 , S 100 , S 1000 ] corresponds to the mathematical form percentages, whereas simple fractions use instead a system with very low values of N, including [S 2 , S 3 , S 4 ].


to obtain the semantic form for "over a quarter," starting from V A = 0.259, we would choose (1) the relation >, (2) the scale system [S 2 , S 3 , S 4 , .


Out of 235 fraction responses overall, only 5 denominators were used, covering the range 2-6, with frequencies as follows: halves 58 (24.7%), thirds 110 (46.8%), quarters 63 (26.8%), fifths 1 (0.4%), and sixths 3 (1.3%).

overall, 98.3% of fraction responses used the proposed scales.


The IA, by contrast, uses a different expand-method, each time creating a single new state in accordance with the pre-determined preference order.

in the initial state, and assuming (as before) that type is the most preferred attribute, the expand-method would create a single new state: s above.


The IA, for example, can be seen as referring to the singleton set {r} that contains the target r and nothing else.

if in line 1 (Figure 2), {r} is replaced by an arbitrary set S, and line 3 is modified as saying C ← D − S instead of C ← D − {r}, then the algorithm produces a description that applies to all elements of S. it is easy enough to let these algorithms produce expressions like "the men" or "the t-shirt wearers," to identify {d 1 , d 3 } and {d 2 , d 3 }, respectively.


Third, there has been a shift towards data collection and empirical evaluation; this has made it possible to empirically evaluate REG algorithms, which is starting to give us an improved understanding of the strengths and weaknesses of existing work (Section 5).

of these developments, REG is now one of the best-developed subfields of NLG.


Early approaches made use of negation in a bag-of-words model by prefixing a word x with a negation marker if a negation word was detected immediately preceding x (Pang, Lee, and Vaithyanathan 2002).

x and NOT x were treated as two completely separate features.


Currently, many annotation schemes exist in parallel (see Section 4).

the existing annotated corpora are all relatively small.


But it should also include that President Bush did not know this piece of information beforehand, as claimed by the political actor Galbraith.

the factuality analysis of the sentence must include President Bush as a source who at some point in time held an uncommitted factuality stance with regard to the existence of these two Islamic sects.


The reader does not have direct access to the factual assessments made by these new sources, but only according to what the author asserts.

we need to appeal to the notion of nested source as presented in Wiebe, Wilson, and Cardie (2005).


The main event denoted by a purpose clause is intensional in nature.

all its relevant sources will assess it as underspecified (Uu), as is the case of seek in the following example, where the "b" part shows the factual assessment: Relative and participial clauses.


In accordance with the information there, the anchor of an epistemic state introduced by the SIP be aware (which behaves like the SIP know) in a context of factuality PS− is characterized with a factuality stance of certainty (CT+), whereas the cognizer, being unaware, remains uncommitted (Uu) (Step 5).

because there are no other factuality markers affecting these values, when the parser reaches event e 2 (Joe knowing something) these are the factuality assignments constituting the factuality profile of that event: the factuality of every event corresponds to the factuality information available at its context, as computed from the interaction of the different factuality markers scoping over it.